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References from Comparative performance of machine learning algorithms for predicting mortality among early-onset colorectal cancer patients in Tennessee. Local targets link to admitted publications; unresolved targets remain external evidence.
Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries
2021 · External reference
Unresolved reference
External reference
Changes in colorectal cancer incidence rates in young and older adults in the United States: what does it tell us about screening
2013 · External reference
Colorectal cancer statistics, 2023
2023 · External reference
Increasing trend in young-onset colorectal cancer in Asia: more cancers in men and more rectal cancers
10.14309/ajg.0000000000000133 · 2019 · External reference
The rise in early-onset colorectal cancer: now a global issue
10.1016/s2468-1253(24)00441-2 · 2025 · External reference
The rising tide of early-onset colorectal cancer: a comprehensive review of epidemiology, clinical features, biology, risk factors, prevention, and early detection
10.1016/s2468-1253(21)00426-x · 2022 · External reference
Early-onset colorectal cancer in tennessee: incidence patterns, survival outcomes, and mortality risk factors
2026 · External reference
A neural network model for survival data
10.1002/sim.4780140108 · 1995 · External reference
Regression splines in the cox model with application to covariate effects in liver disease
10.1080/01621459.1990.10474965 · 1990 · External reference
Machine learning for predicting survival of colorectal cancer patients
2023 · External reference
Prediction of early-onset colorectal cancer mortality rates in the United States using machine learning
10.1002/cam4.6880 · 2024 · External reference
Predicting colorectal cancer survival using time-to-event machine learning: retrospective cohort study
10.2196/44417 · 2023 · External reference
Young-onset colorectal cancer
2023 · External reference
A comparison of penalised regression methods for informing the selection of predictive markers
10.1371/journal.pone.0242730 · 2020 · External reference
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
10.1136/bmj-2023-078378 · 2024 · External reference
Comparison of machine learning models for colon cancer survival: predictive modeling approach
10.2196/72665 · 2025 · External reference
Personalized Colorectal Cancer Survivability Prediction with Machine Learning Methods*
10.1109/bigdata.2018.8622121 · 2018 · External reference
Unresolved reference
2006 · External reference
Explainable AI for predicting mortality risk in metastatic cancer: retrospective cohort study using the memorial sloan kettering-metastatic dataset
10.2196/74196 · 2026 · External reference
Random forests
10.1023/a:1010933404324 · 2001 · External reference
Prediction of early-onset colorectal cancer mortality rates in the United States using machine learning
10.1002/cam4.6880 · ExternalCitation · doi-reference
A neural network model for survival data
10.1002/sim.4780140108 · ExternalCitation · doi-reference
The rising tide of early-onset colorectal cancer: a comprehensive review of epidemiology, clinical features, biology, risk factors, prevention, and early detection
10.1016/s2468-1253(21)00426-x · ExternalCitation · doi-reference
The rise in early-onset colorectal cancer: now a global issue
10.1016/s2468-1253(24)00441-2 · ExternalCitation · doi-reference
Random forests
10.1023/a:1010933404324 · ExternalCitation · doi-reference
Regression splines in the cox model with application to covariate effects in liver disease
10.1080/01621459.1990.10474965 · ExternalCitation · doi-reference
Personalized Colorectal Cancer Survivability Prediction with Machine Learning Methods*
10.1109/bigdata.2018.8622121 · ExternalCitation · doi-reference
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
10.1136/bmj-2023-078378 · ExternalCitation · doi-reference
A comparison of penalised regression methods for informing the selection of predictive markers
10.1371/journal.pone.0242730 · ExternalCitation · doi-reference
Increasing trend in young-onset colorectal cancer in Asia: more cancers in men and more rectal cancers
10.14309/ajg.0000000000000133 · ExternalCitation · doi-reference
Predicting colorectal cancer survival using time-to-event machine learning: retrospective cohort study
10.2196/44417 · ExternalCitation · doi-reference
Comparison of machine learning models for colon cancer survival: predictive modeling approach
10.2196/72665 · ExternalCitation · doi-reference
Explainable AI for predicting mortality risk in metastatic cancer: retrospective cohort study using the memorial sloan kettering-metastatic dataset
10.2196/74196 · ExternalCitation · doi-reference
